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Finding optimal memoryless policies of POMDPs under the expected average reward criterion

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  • Li, Yanjie
  • Yin, Baoqun
  • Xi, Hongsheng

Abstract

In this paper, partially observable Markov decision processes (POMDPs) with discrete state and action space under the average reward criterion are considered from a recent-developed sensitivity point of view. By analyzing the average-reward performance difference formula, we propose a policy iteration algorithm with step sizes to obtain an optimal or local optimal memoryless policy. This algorithm improves the policy along the same direction as the policy iteration does and suitable step sizes guarantee the convergence of the algorithm. Moreover, the algorithm can be used in Markov decision processes (MDPs) with correlated actions. Two numerical examples are provided to illustrate the applicability of the algorithm.

Suggested Citation

  • Li, Yanjie & Yin, Baoqun & Xi, Hongsheng, 2011. "Finding optimal memoryless policies of POMDPs under the expected average reward criterion," European Journal of Operational Research, Elsevier, vol. 211(3), pages 556-567, June.
    • Handle: RePEc:eee:ejores:v:211:y:2011:i:3:p:556-567
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    References listed on IDEAS

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    1. Richard D. Smallwood & Edward J. Sondik, 1973. "The Optimal Control of Partially Observable Markov Processes over a Finite Horizon," Operations Research, INFORMS, vol. 21(5), pages 1071-1088, October.
    2. Hao, Tang & Lei, Zhou & Tamio, Arai, 2008. "Optimization of a special case of continuous-time Markov decision processes with compact action set," European Journal of Operational Research, Elsevier, vol. 187(1), pages 113-119, May.
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